1. Field of Invention
The invention relates generally to the field of distributed real-time control systems, and more particularly, it relates to an optimistic distributed control system for use in an Uninhabited Air Vehicle (UAV) flight control system.
2. Description of Related Art
Studies have shown that current UAVs have not met the degree of safety and reliability required for autonomous operation over populated areas or in airspace shared with commercial aircraft. Autonomy technologies that can provide reflexive responses and rapid adaptation, such as exhibited by a pilot, to compensate for a vehicle""s structural, perceptual and control limitations are lacking. This is particularly evident when UAV mishap rates are compared to those of piloted systems.
Compared to piloted aircraft systems, current UAVs are designed to be very low cost and use smaller, low-power commercial off-the-shelf components and have very limited redundancy. Unfortunately, the lower requirement for reliability has led to higher failure rates. The higher failure rates are seen as somewhat acceptable because it does not mean the loss of human life, except when the vehicle flies over populated areas. It is desirable, however, for a UAV to be able to safely fly over populated areas, to safely share airspace with other piloted vehicles, and to generally improve the mission success rate. For these reasons, the UAV control systems must be capable of rigorously analyzing and predicting component failures, and their effects, to determine the appropriate response to faults much as a pilot does prior to, or in response to a system failure.
A new concept known as active state has been proposed to represent the state of highly dynamic and complex system. Time management is particularly critical in the active state model. For example, higher order biological systems are capable of reasoning about complex future events while simultaneously performing simpler current tasks. Expectations about future outcomes are modified as current events change. Adapting this concept to a rigorous real-time control system is a new challenge that has not yet been attempted.
To overcome the foregoing and other drawbacks in the prior art, it is an object of the present invention to provide a control system that is capable of predicting the state of the system at some future time.
It is a further object of the present invention to provide a predictive control system with sufficient speed for real-time implementation.
It is still a further object of the present invention to provide a real-time predictive control system for distributed execution on multiple processors.
It is still a further object of the present invention to provide a real-time predictive control system for one or more UAVs in order to improve reliability, improve autonomy, to permit safe flight over populated areas, and/or to permit safe flight in airspace shared with other piloted vehicles.
In order to accomplish these and other objects, the present invention comprises a method of real-time distributed model-based predictive control. The method comprises the steps of generating a message that predicts a system value, the message having a receive time corresponding to a future time, and executing a control process using the system value contained within said message, resulting in a process state having a local process time corresponding to the receive time on said message. The process state at that local process time is then stored in a state queue. The local process time is rolled back to the latest valid local process time prior to the receive time of said message, only if said receive time precedes the current process state time. Rolling back comprises retrieving the system state values at that latest valid local process time from said state queue and re-executing the control process, using the system value contained within the message, and storing the new state in the state queue.
The method according to the present invention also comprises rolling back only if, upon receiving a message containing an observed system value, said observed system value differs from its predicted value by more than a predetermined tolerance.
The method is carried out on a distributed processor control system. Messages from one processor can be sent to other processors to further carry the model forward in time.